Abstract
In the mobile communication industry, users are always concerned with the services provided by the operators. Users who are dissatisfied with the services will probably change their mobile network operators. Therefore, mobile network operators desire to predict whether users will be dissatisfied with the services by analyzing users’ events. Then they stand a chance to timely remedy the services for potential dissatisfied users. Though many existing classification methods are available, they cannot leverage user attribute information well in this task. To address the problem, we propose a Personalized Attention-based Long Short-Term Memory (PA-LSTM) model, consisting of events feature extraction module, user feature extraction module, and personalized prediction module. PA-LSTM makes personalized predictions of dissatisfied users based on both user events and user attributes. Furthermore, PA-LSTM considers the satisfaction tendency of different user groups. Extensive experiments on the industry dataset show that our model performs better than other solutions, verifying the effectiveness of our model.
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Acknowledgements
This research is supported by National Natural Science Foundation of China (No. 62077031, U1936206). We thank the reviewers for their constructive comments.
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Chen, Y., Lin, Y., Zhang, B., Zhao, D., Zhang, H., Wen, Y. (2023). Personalized Dissatisfied Users Prediction in Mobile Communication Service. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13946. Springer, Cham. https://doi.org/10.1007/978-3-031-30678-5_42
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DOI: https://doi.org/10.1007/978-3-031-30678-5_42
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